5. Counting Objects in Each Direction#

Import Libraries#

import pandas as pd
import geopandas as gpd

Reading data#

data_cleaned = pd.read_csv('data/1_data_cleaned.csv')
lines_gdf_stat = gpd.read_file('data/2_lines_gdf_stat.gpkg')

points_gdf = gpd.GeoDataFrame(data_cleaned, geometry=gpd.points_from_xy(data_cleaned['lon'], data_cleaned['lat']), crs=4326)
lines_gdf_cars = lines_gdf_stat[lines_gdf_stat['object_type'] =='CAR']
lines_gdf_pedestrians = lines_gdf_stat[lines_gdf_stat['object_type'] =='PEDESTRIAN']
lines_gdf_two_wheelers = lines_gdf_stat[lines_gdf_stat['object_type'] =='CYCLIST']

Defining directions and calculating number of cars in each direction. Option 1#

Creating Starting points#

starting_points = points_gdf.groupby('object_id').first().reset_index().set_crs(epsg=4326)
starting_points.head()
object_id Unnamed: 0 timestamp heading height width length v tracking_status object_type lon lat geometry
0 152997118 0 1712062811083 132.072 0.735 1.942 4.387 0.03 TRACKING CAR 13.064405 47.810136 POINT (13.06440 47.81014)
1 152997181 2802 1712062820184 198.052 1.458 0.641 0.366 1.10 TRACKING PEDESTRIAN 13.063991 47.810058 POINT (13.06399 47.81006)
2 152997182 4156 1712062820083 319.543 1.690 1.916 4.555 0.02 TRACKING CAR 13.064130 47.810046 POINT (13.06413 47.81005)
3 152997183 6803 1712062822387 103.052 0.539 1.923 4.550 1.92 TRACKING CAR 13.063387 47.809774 POINT (13.06339 47.80977)
4 152997184 7812 1712062824786 85.395 1.207 1.981 4.367 2.18 TRACKING CAR 13.063389 47.809776 POINT (13.06339 47.80978) 
starting_points.explore(tiles='Esri.WorldImagery')
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ending_points = points_gdf.groupby('object_id').last().reset_index().set_crs(epsg=4326)
ending_points.head()
object_id Unnamed: 0 timestamp heading height width length v tracking_status object_type lon lat geometry
0 152997118 2780 1712063097385 313.127 0.348 1.955 4.500 0.04 TRACKING CAR 13.064403 47.810137 POINT (13.06440 47.81014)
1 152997181 4145 1712062958884 133.052 0.528 0.645 0.209 0.75 TRACKING PEDESTRIAN 13.064302 47.809506 POINT (13.06430 47.80951)
2 152997182 6780 1712063090784 139.865 0.119 1.883 4.461 0.03 TRACKING CAR 13.064130 47.810045 POINT (13.06413 47.81005)
3 152997183 7790 1712062924186 23.052 0.017 1.880 4.287 10.95 TRACKING CAR 13.064855 47.810200 POINT (13.06485 47.81020)
4 152997184 8793 1712062925683 33.052 0.596 1.959 4.330 8.31 TRACKING CAR 13.064773 47.810138 POINT (13.06477 47.81014) 
ending_points.explore(tiles='Esri.WorldImagery')
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CARS Directions#

def create_regular_grid(gdf, square_size):

    #calculating UTM zone for the data
    utm_zone = gdf.estimate_utm_crs()
    #перепроецируем набор данных
    gdf = gdf.to_crs(utm_zone)
    minX, minY, maxX, maxY = gdf.total_bounds
    
    grid_cells = []
    x, y = minX, minY

    while y <= maxY:
        while x <= maxX:
            geom = Polygon([(x, y), (x, y + square_size), (x + square_size, y + square_size), (x + square_size, y), (x, y)])
            grid_cells.append(geom)
            x += square_size
        x = minX
        y += square_size

    fishnet = gpd.GeoDataFrame(geometry=grid_cells, crs=utm_zone)
    fishnet['grid_id'] = range(len(grid_cells))
    fishnet = fishnet.to_crs(epsg=4326)
    return fishnet

startingPointsCars = starting_points[starting_points['object_type']=="CAR"]
endingPointsCars = ending_points[ending_points['object_type']=="CAR"]

startEndPointsCars = pd.concat([startingPointsCars, endingPointsCars], ignore_index=True)
grid = create_regular_grid(startEndPointsCars, 7.5)
grid.explore(tiles='Esri.WorldImagery')

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[8], line 5
      2 endingPointsCars = ending_points[ending_points['object_type']=="CAR"]
      4 startEndPointsCars = pd.concat([startingPointsCars, endingPointsCars], ignore_index=True)
----> 5 grid = create_regular_grid(startEndPointsCars, 7.5)
      6 grid.explore(tiles='Esri.WorldImagery')

Cell In[7], line 14, in create_regular_grid(gdf, square_size)
     12 while y <= maxY:
     13     while x <= maxX:
---> 14         geom = Polygon([(x, y), (x, y + square_size), (x + square_size, y + square_size), (x + square_size, y), (x, y)])
     15         grid_cells.append(geom)
     16         x += square_size

NameError: name 'Polygon' is not defined

points_grid = gpd.sjoin(startEndPointsCars, grid, predicate='within')
points_count = points_grid.groupby('grid_id').size().reset_index(name='points_count')
grid_with_count = grid.merge(points_count, on='grid_id', how='left')

grid_with_count.head()
geometry grid_id points_count
0 POLYGON ((13.06309 47.80930, 13.06309 47.80937... 0 NaN
1 POLYGON ((13.06319 47.80930, 13.06319 47.80937... 1 NaN
2 POLYGON ((13.06329 47.80930, 13.06329 47.80937... 2 NaN
3 POLYGON ((13.06339 47.80930, 13.06339 47.80937... 3 NaN
4 POLYGON ((13.06349 47.80931, 13.06349 47.80937... 4 NaN 
grid_main_clusters = grid_with_count[grid_with_count['points_count']>25]
grid_with_count.explore(column='points_count', tiles='Esri.WorldImagery')
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Create Clusters#

Function to find clusters of polygons based on spatial relationships (touches or intersects:

def find_polygon_clusters(gdf):
    # Ensure geometries are valid
    gdf = gdf[gdf.is_valid]
    
    # Initialize a dictionary to store cluster names
    cluster_names = {}
    
    # Initialize an empty list to store groups of neighboring polygons
    polygon_groups = []
    
    # Counter for cluster names
    cluster_counter = 1
    
    # Loop through each polygon and find its neighbors
    for idx, polygon in gdf.iterrows():
        if idx not in cluster_names:
            cluster_names[idx] = f'Cluster{cluster_counter}'
            cluster_counter += 1
        
        # Find neighboring polygons (e.g., polygons that touch or intersect)
        neighbors = gdf[gdf.geometry.touches(polygon['geometry']) | gdf.geometry.intersects(polygon['geometry'])]
        
        # Check if the polygon and its neighbors form a MultiPolygon
        if len(neighbors) > 0:
            merged_geometry = MultiPolygon([polygon['geometry']] + list(neighbors['geometry']))
            polygon_groups.append(merged_geometry)
            
            # Assign the same cluster name to all neighbors
            for neighbor_idx in neighbors.index:
                if neighbor_idx not in cluster_names:
                    cluster_names[neighbor_idx] = cluster_names[idx]
    
    # Assign cluster names to the GeoDataFrame
    gdf['cluster'] = gdf.index.map(cluster_names)
    
    return gdf

cars_clusters = find_polygon_clusters(grid_main_clusters)

cars_clusters .explore(column='cluster', tiles='Esri.WorldImagery')
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startingPointsCars_CLUSTER = gpd.sjoin(startingPointsCars.to_crs(cars_clusters .crs), cars_clusters , predicate='within')
endingPointsCars_CLUSTER = gpd.sjoin(endingPointsCars.to_crs(cars_clusters .crs), cars_clusters , predicate='within')

startingPointsCars_CLUSTER['start_cluster'] = startingPointsCars_CLUSTER['cluster']
endingPointsCars_CLUSTER['end_cluster'] = endingPointsCars_CLUSTER['cluster']

startingPointsCars_CLUSTER.explore(column="cluster",tiles='Esri.WorldImagery')
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endingPointsCars_CLUSTER.explore(column="cluster", tiles='Esri.WorldImagery')
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Getting Directions

lines_gdf_cars = lines_gdf_cars.merge(startingPointsCars_CLUSTER[['object_id','start_cluster']], on="object_id", how="left")
lines_gdf_cars = lines_gdf_cars.merge(endingPointsCars_CLUSTER[['object_id','end_cluster']], on="object_id", how="left")
lines_gdf_cars.groupby(['start_cluster', 'end_cluster']).size()

start_cluster  end_cluster
Cluster1       Cluster1        36
               Cluster2         2
               Cluster3        14
               Cluster4       140
Cluster2       Cluster1       118
               Cluster2        66
               Cluster3       383
               Cluster4       114
Cluster3       Cluster1        31
               Cluster2       490
               Cluster3       298
               Cluster4       260
Cluster4       Cluster1       118
               Cluster2       249
               Cluster3       248
               Cluster4       922
Cluster5       Cluster5        18
dtype: int64

Defining directions and calculating number of cars in each direction. Option 2#

The previous approach unfortunalty can’t be applied for pedestrians and two-wheelers as their routes are less clear distingishable and not strait. So, for pedestrians and two-wheelers we will define their directions baes on the intersection with crosslines created by us in QGIS

Defining directions and calculating number of cars in each direction. Option 3#

This approach is very easy to implement but it is less precise in comparison to the previous two. We have started with it and want to show it anyway as it was a part of our work and it still shows some overall information about directions

Calculating amount of objects in different directions for cars#

cars_heading_breaks = jenkspy.jenks_breaks(lines_gdf_cars['avg_heading'], 5)

# Создаем столбец для класса направления на основе разрывов
lines_gdf_cars['direction_class'] = pd.cut(lines_gdf_cars['avg_heading'], bins=cars_heading_breaks, labels=False, include_lowest=True)

# Считаем количество объектов в каждом классе направления
direction_class_counts = lines_gdf_cars['direction_class'].value_counts().sort_index()

print("Breaks:", cars_heading_breaks)
print("Direction Class Counts:\n", direction_class_counts)

Breaks: [8.052, 70.552, 158.05200000000002, 236.827, 276.276, 353.052]
Direction Class Counts:
 0     642
1    1072
2    1361
3     641
4     462
Name: direction_class, dtype: int64

# Creating Histogram
sns.histplot(data=lines_gdf_cars, x='avg_heading', hue='direction_class', bins=20, alpha=0.8, palette='BuPu', edgecolor='lightgray')

# Removing graph border
sns.despine()

# Adding titlle and axis names
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Histogram, bins colored by class')

# Showing graph
plt.show()
../_images/6dcbfe9362ceda41dd9144d7de3dbe38872b09e62ada3fd47abeb71756553ef9.png 
# Convert angles from degrees to radians for the polar plot
lines_gdf_cars['avg_heading_rad'] = np.deg2rad(lines_gdf_cars['avg_heading'])

# Create the polar plot
plt.figure(figsize=(10, 6))
ax = plt.subplot(111, projection='polar')

# Configure the polar plot: 0 degrees at the top and clockwise direction
ax.set_theta_zero_location('N')
ax.set_theta_direction(-1)

# Define the number of bins
num_bins = 8
bins = np.linspace(0, 2 * np.pi, num_bins + 1)

# Count the number of objects in each bin
hist, edges = np.histogram(lines_gdf_cars['avg_heading_rad'], bins=bins)

# Plot the histogram on the polar plot
bars = ax.bar(edges[:-1], hist, width=np.diff(edges), align='edge', edgecolor='black')

# Set angle labels in degrees
ax.set_xticks(np.deg2rad(np.arange(0, 360, 45)))
ax.set_xticklabels(['0°', '45°', '90°', '135°', '180°', '225°', '270°', '315°'])

# Add title
plt.title('Distribution of Objects by Avg Heading')

# Display the plot
plt.show()
../_images/c8526fe3d4f0af61eced4f207ee4dfda22d2808bebc1a776d1b4d52984ba751c.png

Calculating amount of objects in different directions for pedestrians#

pedestrians_heading_breaks = jenkspy.jenks_breaks(lines_gdf_pedestrians['avg_heading'], 5)

# Создаем столбец для класса направления на основе разрывов
lines_gdf_pedestrians['direction_class'] = pd.cut(lines_gdf_pedestrians['avg_heading'], bins=pedestrians_heading_breaks, labels=False, include_lowest=True)

# Считаем количество объектов в каждом классе направления
direction_class_counts = lines_gdf_pedestrians['direction_class'].value_counts().sort_index()

print("Breaks:", pedestrians_heading_breaks)
print("Direction Class Counts:\n", direction_class_counts)

Breaks: [3.052, 89.59200000000001, 160.552, 228.439, 285.552, 358.052]
Direction Class Counts:
 0    388
1    371
2    411
3    358
4    241
Name: direction_class, dtype: int64

/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/geopandas/geodataframe.py:1538: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

# Creating Histogram
sns.histplot(data=lines_gdf_pedestrians, x='avg_heading', hue='direction_class', bins=20, alpha=0.8, palette='BuPu', edgecolor='lightgray')

# Removing graph border
sns.despine()

# Adding titlle and axis names
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Histogram, bins colored by class')

# Showing graph
plt.show()
../_images/90ee75afc1518faa1f4fda568fcbf255731c9944abfb51791a1f149c76069a4d.png 
# Convert angles from degrees to radians for the polar plot
lines_gdf_pedestrians['avg_heading_rad'] = np.deg2rad(lines_gdf_pedestrians['avg_heading'])

# Create the polar plot
plt.figure(figsize=(10, 6))
ax = plt.subplot(111, projection='polar')

# Configure the polar plot: 0 degrees at the top and clockwise direction
ax.set_theta_zero_location('N')
ax.set_theta_direction(-1)

# Define the number of bins
num_bins = 8
bins = np.linspace(0, 2 * np.pi, num_bins + 1)

# Count the number of objects in each bin
hist, edges = np.histogram(lines_gdf_pedestrians['avg_heading_rad'], bins=bins)

# Plot the histogram on the polar plot
bars = ax.bar(edges[:-1], hist, width=np.diff(edges), align='edge', edgecolor='black')

# Set angle labels in degrees
ax.set_xticks(np.deg2rad(np.arange(0, 360, 45)))
ax.set_xticklabels(['0°', '45°', '90°', '135°', '180°', '225°', '270°', '315°'])

# Add title
plt.title('Distribution of Objects by Avg Heading')

# Display the plot
plt.show()

/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/geopandas/geodataframe.py:1538: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
../_images/f92021ffa04f3343797ae8be2edb1a2f365f209d2442856c2bfac4367a8ee7d5.png

Calculating amount of objects in different directions for two-wheelers#

two_wheelers_heading_breaks = jenkspy.jenks_breaks(lines_gdf_two_wheelers['avg_heading'], 5)

# Создаем столбец для класса направления на основе разрывов
lines_gdf_two_wheelers['direction_class'] = pd.cut(lines_gdf_two_wheelers['avg_heading'], bins=two_wheelers_heading_breaks, labels=False, include_lowest=True)

# Считаем количество объектов в каждом классе направления
direction_class_counts = lines_gdf_two_wheelers['direction_class'].value_counts().sort_index()

print("Breaks:", cars_heading_breaks)
print("Direction Class Counts:\n", direction_class_counts)

Breaks: [8.052, 70.552, 158.05200000000002, 236.827, 276.276, 353.052]
Direction Class Counts:
 0    43
1    66
2    59
3    45
4    37
Name: direction_class, dtype: int64

/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/geopandas/geodataframe.py:1538: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

# Creating Histogram
sns.histplot(data=lines_gdf_cars, x='avg_heading', hue='direction_class', bins=20, alpha=0.8, palette='BuPu', edgecolor='lightgray')

# Removing graph border
sns.despine()

# Adding titlle and axis names
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Histogram, bins colored by class')

# Showing graph

Text(0.5, 1.0, 'Histogram, bins colored by class')
../_images/6dcbfe9362ceda41dd9144d7de3dbe38872b09e62ada3fd47abeb71756553ef9.png 
# Convert angles from degrees to radians for the polar plot
lines_gdf_cars['avg_heading_rad'] = np.deg2rad(lines_gdf_cars['avg_heading'])

# Create the polar plot
plt.figure(figsize=(10, 6))
ax = plt.subplot(111, projection='polar')

# Configure the polar plot: 0 degrees at the top and clockwise direction
ax.set_theta_zero_location('N')
ax.set_theta_direction(-1)

# Define the number of bins
num_bins = 8
bins = np.linspace(0, 2 * np.pi, num_bins + 1)

# Count the number of objects in each bin
hist, edges = np.histogram(lines_gdf_cars['avg_heading_rad'], bins=bins)

# Plot the histogram on the polar plot
bars = ax.bar(edges[:-1], hist, width=np.diff(edges), align='edge', edgecolor='black')

# Set angle labels in degrees
ax.set_xticks(np.deg2rad(np.arange(0, 360, 45)))
ax.set_xticklabels(['0°', '45°', '90°', '135°', '180°', '225°', '270°', '315°'])

# Add title
plt.title('Distribution of Objects by Avg Heading')

# Display the plot
plt.show()
../_images/c8526fe3d4f0af61eced4f207ee4dfda22d2808bebc1a776d1b4d52984ba751c.png